{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T17:00:52Z","timestamp":1780938052367,"version":"3.54.1"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Active trachoma remains above elimination thresholds in Ethiopia, yet tools for fine-scale targeting are limited. We developed and internally validated an interpretable machine-learning model to predict household-level risk of trachomatous inflammation\u2013follicular (TF) and mapped hotspots to guide SAFE implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A community-based cross-sectional survey was conducted in Dessie Zuria District (March\u2013May 2024) among households with children aged 1\u20139 y. TF was graded using the amended WHO simplified system. Behavioral, environmental and sociodemographic predictors were collected using WHO-aligned tools. A survey-weighted ridge-penalized logistic regression with nested cross-validation was trained, with missing data imputed within folds. Model performance (AUC, calibration, Brier score) and decision-curve analysis were evaluated using kebele-cluster bootstrapping. Spatial clustering was assessed using Moran\u2019s I and false discovery rateadjusted Getis\u2013Ord Gi*.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 612 households (928 children), 9.8% of households and 9.1% of children were TF-positive. Key predictors included unclean child face, absence of latrine, water-collection time &amp;gt;30 minutes, flies on the face and low hygiene score. The model showed strong discrimination (AUC = 0.84), good calibration (slope 0.98) and accuracy (Brier 0.067).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The model identifies high-risk households and spatial hotspots, supporting targeted F\/E interventions to accelerate trachoma elimination.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/trstmh\/trag032","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:40:06Z","timestamp":1775065206000},"page":"685-692","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable machine-learning prediction of household risk of trachoma and spatial hotspot mapping in Ethiopia"],"prefix":"10.1093","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9413-7224","authenticated-orcid":false,"given":"Berihun Mulu","family":"Yayeh","sequence":"first","affiliation":[{"name":"International PhD Program in Innovative Technology of Biomedical Engineering and Medical Devices, College of Engineering, Ming Chi University of Technology , New Taipei City 243303 ,","place":["Taiwan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin Fong-Rey","family":"Liu","sequence":"additional","affiliation":[{"name":"Health and Environmental Engineering, College of Environment Resources, Ming Chi University of Technology Department of Safety, , New Taipei City 243303 ,","place":["Taiwan"]},{"name":"Center for Artificial Intelligence & Data Science, Ming Chi University of Technology, New Taipei City 243303, 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